Azure Functions

Text Summarization using AI Cognitive Language Service (Python v2 Function)

This sample shows how to take text documents as a input via BlobTrigger, does Text Summarization processing using the AI Congnitive Language service, and then outputs to another text document using BlobOutput binding.

Run on your local environment

Pre-reqs

1. Python 3.7 - 3.10 required
2. Azure Functions Core Tools
3. Azurite

The easiest way to install Azurite is using a Docker container or the support built into Visual Studio:

docker run -d -p 10000:10000 -p 10001:10001 -p 10002:10002 mcr.microsoft.com/azure-storage/azurite

4. Once you have your Azure subscription, create a Language resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource. You will need the key and endpoint from the resource you create to connect your application to the API. You'll need to store the key and endpoint into the Env Vars or User Secrets code in a next step the quickstart. You can use the free pricing tier (Free F0) to try the service, and upgrade later to a paid tier for production.
5. Export these secrets as Env Vars using values from Step 4.

Mac/Linux

export AI_URL=*Paste from step 4*
export AI_SECRET=*Paste from step 4*

Windows

Search for Environment Variables in Settings, create new System Variables similarly to these instructions:

Variable	Value
AI_URL	Paste from step 4
AI_SECRET	Paste from step 4

6. Azure Storage Explorer or storage explorer features of Azure Portal
7. Add this local.settings.json file to the text_summarize folder to simplify local development. Optionally fill in the AI_URL and AI_SECRET values per step 4. This file will be gitignored to protect secrets from committing to your repo.

{
  "IsEncrypted": false,
  "Values": {
    "FUNCTIONS_WORKER_RUNTIME": "python",
    "AzureWebJobsFeatureFlags": "EnableWorkerIndexing",
    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
    "blobstorage": "UseDevelopmentStorage=true",
    "AI_URL": "",
    "AI_SECRET": ""
  }
}

Using VS Code

1. Open the ./text_summarize folder in VS Code:

cd ./text_summarize
code .

2. When prompted in VS Code, Create Virtual Environment and choose your version of Python if prompted.
3. Run and Debug by pressing F5
4. Open Storage Explorer, Storage Accounts -> Emulator -> Blob Containers -> and create a container test-samples-trigger if it does not already exists
5. Copy any .txt document file with text into the test-samples-trigger container

You will see AI analysis happen in the Terminal standard out. The analysis will be saved in a .txt file in the test-samples-output blob container.

Using Functions CLI

1. Open a new terminal and do the following:

cd text_summarize
pip3 install -r requirements.txt
func start

2. Open Storage Explorer, Storage Accounts -> Emulator -> Blob Containers -> and create a container test-samples-trigger if it does not already exists
3. Copy any .txt document file with text into the test-samples-trigger container

You will see AI analysis happen in the Terminal standard out. The analysis will be saved in a .txt file in the test-samples-output blob container.

Deploy to Azure

The easiest way to deploy this app is using the Azure Dev CLI aka AZD. If you open this repo in GitHub CodeSpaces the AZD tooling is already preinstalled.

To provision and deploy:

azd up